Overview

Dataset statistics

Number of variables23
Number of observations3803
Missing cells6996
Missing cells (%)8.0%
Duplicate rows126
Duplicate rows (%)3.3%
Total size in memory2.3 MiB
Average record size in memory623.5 B

Variable types

Categorical13
Numeric10

Alerts

Dataset has 126 (3.3%) duplicate rowsDuplicates
society has a high cardinality: 676 distinct values High cardinality
sector has a high cardinality: 104 distinct values High cardinality
areaWithType has a high cardinality: 2355 distinct values High cardinality
price is highly correlated with property_type and 3 other fieldsHigh correlation
price_per_sqft is highly correlated with priceHigh correlation
area is highly correlated with built_up_area and 1 other fieldsHigh correlation
bedRoom is highly correlated with property_type and 5 other fieldsHigh correlation
bathroom is highly correlated with property_type and 5 other fieldsHigh correlation
super_built_up_area is highly correlated with price and 3 other fieldsHigh correlation
built_up_area is highly correlated with areaHigh correlation
carpet_area is highly correlated with areaHigh correlation
servant room is highly correlated with bedRoom and 2 other fieldsHigh correlation
property_type is highly correlated with price and 4 other fieldsHigh correlation
balcony is highly correlated with bedRoom and 1 other fieldsHigh correlation
floorNum is highly correlated with property_typeHigh correlation
agePossession is highly correlated with property_typeHigh correlation
facing has 1105 (29.1%) missing values Missing
super_built_up_area has 1888 (49.6%) missing values Missing
built_up_area has 2070 (54.4%) missing values Missing
carpet_area has 1859 (48.9%) missing values Missing
area is highly skewed (γ1 = 30.23273447) Skewed
built_up_area is highly skewed (γ1 = 41.21758008) Skewed
carpet_area is highly skewed (γ1 = 24.7960836) Skewed
floorNum has 134 (3.5%) zeros Zeros
luxury_score has 486 (12.8%) zeros Zeros

Reproduction

Analysis started2023-10-13 07:57:36.379994
Analysis finished2023-10-13 07:58:01.242299
Duration24.86 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

property_type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size227.5 KiB
flat
2943 
house
860 

Length

Max length5
Median length4
Mean length4.22613726
Min length4

Characters and Unicode

Total characters16072
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat2943
77.4%
house860
 
22.6%

Length

2023-10-13T13:28:01.354623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-10-13T13:28:01.510189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
flat2943
77.4%
house860
 
22.6%

Most occurring characters

ValueCountFrequency (%)
f2943
18.3%
l2943
18.3%
a2943
18.3%
t2943
18.3%
h860
 
5.4%
o860
 
5.4%
u860
 
5.4%
s860
 
5.4%
e860
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16072
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f2943
18.3%
l2943
18.3%
a2943
18.3%
t2943
18.3%
h860
 
5.4%
o860
 
5.4%
u860
 
5.4%
s860
 
5.4%
e860
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin16072
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f2943
18.3%
l2943
18.3%
a2943
18.3%
t2943
18.3%
h860
 
5.4%
o860
 
5.4%
u860
 
5.4%
s860
 
5.4%
e860
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII16072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f2943
18.3%
l2943
18.3%
a2943
18.3%
t2943
18.3%
h860
 
5.4%
o860
 
5.4%
u860
 
5.4%
s860
 
5.4%
e860
 
5.4%

society
Categorical

HIGH CARDINALITY

Distinct676
Distinct (%)17.8%
Missing1
Missing (%)< 0.1%
Memory size274.6 KiB
independent
486 
tulip violet
 
75
ss the leaf
 
74
shapoorji pallonji joyville gurugram
 
45
dlf new town heights
 
42
Other values (671)
3080 

Length

Max length49
Median length39
Mean length16.92267228
Min length1

Characters and Unicode

Total characters64340
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique291 ?
Unique (%)7.7%

Sample

1st rowsignature global park 4
2nd rowsmart world gems
3rd rowpyramid elite
4th rowbreez global hill view
5th rowbestech park view sanskruti

Common Values

ValueCountFrequency (%)
independent486
 
12.8%
tulip violet75
 
2.0%
ss the leaf74
 
1.9%
shapoorji pallonji joyville gurugram45
 
1.2%
dlf new town heights42
 
1.1%
signature global park37
 
1.0%
shree vardhman victoria35
 
0.9%
smart world gems33
 
0.9%
smart world orchard33
 
0.9%
emaar mgf emerald floors premier32
 
0.8%
Other values (666)2910
76.5%

Length

2023-10-13T13:28:01.672736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
independent491
 
4.9%
the362
 
3.6%
dlf225
 
2.2%
park219
 
2.2%
city172
 
1.7%
global165
 
1.6%
signature161
 
1.6%
emaar159
 
1.6%
m3m156
 
1.6%
heights139
 
1.4%
Other values (783)7779
77.6%

Most occurring characters

ValueCountFrequency (%)
e6935
 
10.8%
6228
 
9.7%
a6090
 
9.5%
r4355
 
6.8%
n4270
 
6.6%
i3970
 
6.2%
t3851
 
6.0%
s3627
 
5.6%
l3074
 
4.8%
o2867
 
4.5%
Other values (31)19073
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter57544
89.4%
Space Separator6228
 
9.7%
Decimal Number550
 
0.9%
Other Punctuation10
 
< 0.1%
Dash Punctuation8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6935
12.1%
a6090
 
10.6%
r4355
 
7.6%
n4270
 
7.4%
i3970
 
6.9%
t3851
 
6.7%
s3627
 
6.3%
l3074
 
5.3%
o2867
 
5.0%
d2550
 
4.4%
Other values (16)15955
27.7%
Decimal Number
ValueCountFrequency (%)
3216
39.3%
283
 
15.1%
176
 
13.8%
662
 
11.3%
835
 
6.4%
419
 
3.5%
517
 
3.1%
915
 
2.7%
714
 
2.5%
013
 
2.4%
Other Punctuation
ValueCountFrequency (%)
,7
70.0%
/2
 
20.0%
.1
 
10.0%
Space Separator
ValueCountFrequency (%)
6228
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin57544
89.4%
Common6796
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6935
12.1%
a6090
 
10.6%
r4355
 
7.6%
n4270
 
7.4%
i3970
 
6.9%
t3851
 
6.7%
s3627
 
6.3%
l3074
 
5.3%
o2867
 
5.0%
d2550
 
4.4%
Other values (16)15955
27.7%
Common
ValueCountFrequency (%)
6228
91.6%
3216
 
3.2%
283
 
1.2%
176
 
1.1%
662
 
0.9%
835
 
0.5%
419
 
0.3%
517
 
0.3%
915
 
0.2%
714
 
0.2%
Other values (5)31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII64340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6935
 
10.8%
6228
 
9.7%
a6090
 
9.5%
r4355
 
6.8%
n4270
 
6.6%
i3970
 
6.2%
t3851
 
6.0%
s3627
 
5.6%
l3074
 
4.8%
o2867
 
4.5%
Other values (31)19073
29.6%

sector
Categorical

HIGH CARDINALITY

Distinct104
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size246.0 KiB
sohna road
 
175
sector 102
 
113
sector 85
 
110
sector 92
 
105
sector 69
 
94
Other values (99)
3206 

Length

Max length17
Median length9
Mean length9.203523534
Min length7

Characters and Unicode

Total characters35001
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsector 36
2nd rowsector 89
3rd rowsector 86
4th rowsohna road
5th rowsector 92

Common Values

ValueCountFrequency (%)
sohna road175
 
4.6%
sector 102113
 
3.0%
sector 85110
 
2.9%
sector 92105
 
2.8%
sector 6994
 
2.5%
sector 9091
 
2.4%
sector 8190
 
2.4%
sector 6590
 
2.4%
sector 10988
 
2.3%
sector 7980
 
2.1%
Other values (94)2767
72.8%

Length

2023-10-13T13:28:01.858739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sector3569
47.1%
sohna175
 
2.3%
road175
 
2.3%
102113
 
1.5%
85110
 
1.5%
92105
 
1.4%
6994
 
1.2%
9091
 
1.2%
8190
 
1.2%
6590
 
1.2%
Other values (98)2961
39.1%

Most occurring characters

ValueCountFrequency (%)
o3919
11.2%
r3811
10.9%
s3793
10.8%
3770
10.8%
e3618
10.3%
c3569
10.2%
t3569
10.2%
11105
 
3.2%
0827
 
2.4%
8808
 
2.3%
Other values (20)6212
17.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23733
67.8%
Decimal Number7498
 
21.4%
Space Separator3770
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o3919
16.5%
r3811
16.1%
s3793
16.0%
e3618
15.2%
c3569
15.0%
t3569
15.0%
a631
 
2.7%
d251
 
1.1%
n208
 
0.9%
h193
 
0.8%
Other values (9)171
 
0.7%
Decimal Number
ValueCountFrequency (%)
11105
14.7%
0827
11.0%
8808
10.8%
9805
10.7%
6760
10.1%
7707
9.4%
2690
9.2%
3668
8.9%
5628
8.4%
4500
6.7%
Space Separator
ValueCountFrequency (%)
3770
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23733
67.8%
Common11268
32.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o3919
16.5%
r3811
16.1%
s3793
16.0%
e3618
15.2%
c3569
15.0%
t3569
15.0%
a631
 
2.7%
d251
 
1.1%
n208
 
0.9%
h193
 
0.8%
Other values (9)171
 
0.7%
Common
ValueCountFrequency (%)
3770
33.5%
11105
 
9.8%
0827
 
7.3%
8808
 
7.2%
9805
 
7.1%
6760
 
6.7%
7707
 
6.3%
2690
 
6.1%
3668
 
5.9%
5628
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII35001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o3919
11.2%
r3811
10.9%
s3793
10.8%
3770
10.8%
e3618
10.3%
c3569
10.2%
t3569
10.2%
11105
 
3.2%
0827
 
2.4%
8808
 
2.3%
Other values (20)6212
17.7%

price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct473
Distinct (%)12.5%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.505804491
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-13T13:28:02.027995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.94
median1.5
Q32.7
95-th percentile8.49
Maximum31.5
Range31.43
Interquartile range (IQR)1.76

Descriptive statistics

Standard deviation2.950121185
Coefficient of variation (CV)1.177314988
Kurtosis15.25781859
Mean2.505804491
Median Absolute Deviation (MAD)0.71
Skewness3.311334654
Sum9484.47
Variance8.703215004
MonotonicityNot monotonic
2023-10-13T13:28:02.198911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2583
 
2.2%
0.968
 
1.8%
1.566
 
1.7%
1.266
 
1.7%
1.166
 
1.7%
1.463
 
1.7%
1.360
 
1.6%
0.9558
 
1.5%
256
 
1.5%
151
 
1.3%
Other values (463)3148
82.8%
ValueCountFrequency (%)
0.071
 
< 0.1%
0.161
 
< 0.1%
0.171
 
< 0.1%
0.191
 
< 0.1%
0.29
0.2%
0.216
0.2%
0.229
0.2%
0.231
 
< 0.1%
0.247
0.2%
0.2511
0.3%
ValueCountFrequency (%)
31.51
 
< 0.1%
27.51
 
< 0.1%
262
0.1%
251
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
221
 
< 0.1%
203
0.1%
19.52
0.1%
193
0.1%

price_per_sqft
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2651
Distinct (%)70.0%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13800.16777
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-13T13:28:02.380484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4718.2
Q16808
median9000
Q313765
95-th percentile33308.2
Maximum600000
Range599996
Interquartile range (IQR)6957

Descriptive statistics

Standard deviation23052.00558
Coefficient of variation (CV)1.67041488
Kurtosis187.041866
Mean13800.16777
Median Absolute Deviation (MAD)2758
Skewness11.43921996
Sum52233635
Variance531394961.5
MonotonicityNot monotonic
2023-10-13T13:28:02.562014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000028
 
0.7%
800019
 
0.5%
1250017
 
0.4%
500017
 
0.4%
750014
 
0.4%
666614
 
0.4%
1111114
 
0.4%
2222213
 
0.3%
833313
 
0.3%
3333311
 
0.3%
Other values (2641)3625
95.3%
(Missing)18
 
0.5%
ValueCountFrequency (%)
41
< 0.1%
51
< 0.1%
71
< 0.1%
91
< 0.1%
531
< 0.1%
571
< 0.1%
582
0.1%
601
< 0.1%
611
< 0.1%
791
< 0.1%
ValueCountFrequency (%)
6000001
< 0.1%
4000001
< 0.1%
3157891
< 0.1%
3083331
< 0.1%
2909481
< 0.1%
2833331
< 0.1%
2666661
< 0.1%
2611941
< 0.1%
2453981
< 0.1%
2416661
< 0.1%

area
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1312
Distinct (%)34.7%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2845.999472
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-13T13:28:02.756206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519
Q11220
median1725
Q32295
95-th percentile4200
Maximum875000
Range874950
Interquartile range (IQR)1075

Descriptive statistics

Standard deviation22783.34905
Coefficient of variation (CV)8.005394689
Kurtosis974.1918286
Mean2845.999472
Median Absolute Deviation (MAD)525
Skewness30.23273447
Sum10772108
Variance519080994.1
MonotonicityNot monotonic
2023-10-13T13:28:02.945009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165055
 
1.4%
135051
 
1.3%
180048
 
1.3%
195044
 
1.2%
324043
 
1.1%
90039
 
1.0%
270039
 
1.0%
200035
 
0.9%
240025
 
0.7%
225025
 
0.7%
Other values (1302)3381
88.9%
ValueCountFrequency (%)
504
0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
602
0.1%
611
 
< 0.1%
672
0.1%
701
 
< 0.1%
721
 
< 0.1%
761
 
< 0.1%
ValueCountFrequency (%)
8750001
< 0.1%
6428571
< 0.1%
6200001
< 0.1%
5666671
< 0.1%
2155171
< 0.1%
989781
< 0.1%
827811
< 0.1%
655172
0.1%
652611
< 0.1%
582281
< 0.1%

areaWithType
Categorical

HIGH CARDINALITY

Distinct2355
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size411.8 KiB
Plot area 360(301.01 sq.m.)
 
37
Plot area 300(250.84 sq.m.)
 
26
Plot area 502(419.74 sq.m.)
 
19
Plot area 200(167.23 sq.m.)
 
19
Super Built up area 1578(146.6 sq.m.)
 
17
Other values (2350)
3685 

Length

Max length124
Median length119
Mean length53.84196687
Min length12

Characters and Unicode

Total characters204761
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1780 ?
Unique (%)46.8%

Sample

1st rowSuper Built up area 1081(100.43 sq.m.)Carpet area: 650 sq.ft. (60.39 sq.m.)
2nd rowCarpet area: 1103 (102.47 sq.m.)
3rd rowCarpet area: 58141 (5401.48 sq.m.)
4th rowBuilt Up area: 1000 (92.9 sq.m.)Carpet area: 585 sq.ft. (54.35 sq.m.)
5th rowSuper Built up area 1995(185.34 sq.m.)Built Up area: 1615 sq.ft. (150.04 sq.m.)Carpet area: 1476 sq.ft. (137.12 sq.m.)

Common Values

ValueCountFrequency (%)
Plot area 360(301.01 sq.m.)37
 
1.0%
Plot area 300(250.84 sq.m.)26
 
0.7%
Plot area 502(419.74 sq.m.)19
 
0.5%
Plot area 200(167.23 sq.m.)19
 
0.5%
Super Built up area 1578(146.6 sq.m.)17
 
0.4%
Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.)17
 
0.4%
Super Built up area 1350(125.42 sq.m.)17
 
0.4%
Plot area 270(225.75 sq.m.)17
 
0.4%
Super Built up area 1650(153.29 sq.m.)Carpet area: 1022.58 sq.ft. (95 sq.m.)15
 
0.4%
Super Built up area 2010(186.74 sq.m.)14
 
0.4%
Other values (2345)3605
94.8%

Length

2023-10-13T13:28:03.157871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
area5728
18.5%
sq.m3779
12.2%
up3102
 
10.0%
built2393
 
7.7%
super1915
 
6.2%
sq.ft1779
 
5.7%
sq.m.)carpet1208
 
3.9%
carpet732
 
2.4%
sq.m.)built707
 
2.3%
plot682
 
2.2%
Other values (2846)8965
28.9%

Most occurring characters

ValueCountFrequency (%)
27187
 
13.3%
.20907
 
10.2%
a13536
 
6.6%
r9723
 
4.7%
e9587
 
4.7%
19460
 
4.6%
s7747
 
3.8%
q7611
 
3.7%
t7507
 
3.7%
p6961
 
3.4%
Other values (25)84535
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter84919
41.5%
Decimal Number48415
23.6%
Space Separator27187
 
13.3%
Other Punctuation24038
 
11.7%
Uppercase Letter8830
 
4.3%
Close Punctuation5686
 
2.8%
Open Punctuation5686
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a13536
15.9%
r9723
11.4%
e9587
11.3%
s7747
9.1%
q7611
9.0%
t7507
8.8%
p6961
8.2%
u6932
8.2%
m5696
6.7%
l3784
 
4.5%
Other values (5)5835
6.9%
Decimal Number
ValueCountFrequency (%)
19460
19.5%
06789
14.0%
25850
12.1%
54855
10.0%
34071
8.4%
43809
7.9%
63772
 
7.8%
73343
 
6.9%
83238
 
6.7%
93228
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B3102
35.1%
C1944
22.0%
S1915
21.7%
U1187
 
13.4%
P682
 
7.7%
Other Punctuation
ValueCountFrequency (%)
.20907
87.0%
:3131
 
13.0%
Space Separator
ValueCountFrequency (%)
27187
100.0%
Close Punctuation
ValueCountFrequency (%)
)5686
100.0%
Open Punctuation
ValueCountFrequency (%)
(5686
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common111012
54.2%
Latin93749
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a13536
14.4%
r9723
10.4%
e9587
10.2%
s7747
8.3%
q7611
8.1%
t7507
8.0%
p6961
7.4%
u6932
7.4%
m5696
 
6.1%
l3784
 
4.0%
Other values (10)14665
15.6%
Common
ValueCountFrequency (%)
27187
24.5%
.20907
18.8%
19460
 
8.5%
06789
 
6.1%
25850
 
5.3%
)5686
 
5.1%
(5686
 
5.1%
54855
 
4.4%
34071
 
3.7%
43809
 
3.4%
Other values (5)16712
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII204761
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27187
 
13.3%
.20907
 
10.2%
a13536
 
6.6%
r9723
 
4.7%
e9587
 
4.7%
19460
 
4.6%
s7747
 
3.8%
q7611
 
3.7%
t7507
 
3.7%
p6961
 
3.4%
Other values (25)84535
41.3%

bedRoom
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.338154089
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-13T13:28:03.313272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.876733555
Coefficient of variation (CV)0.5622069879
Kurtosis18.61025394
Mean3.338154089
Median Absolute Deviation (MAD)1
Skewness3.511539002
Sum12695
Variance3.522128838
MonotonicityNot monotonic
2023-10-13T13:28:03.455421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31545
40.6%
2993
26.1%
4676
17.8%
5213
 
5.6%
1130
 
3.4%
675
 
2.0%
941
 
1.1%
830
 
0.8%
1228
 
0.7%
728
 
0.7%
Other values (9)44
 
1.2%
ValueCountFrequency (%)
1130
 
3.4%
2993
26.1%
31545
40.6%
4676
17.8%
5213
 
5.6%
675
 
2.0%
728
 
0.7%
830
 
0.8%
941
 
1.1%
1020
 
0.5%
ValueCountFrequency (%)
211
 
< 0.1%
201
 
< 0.1%
192
 
0.1%
182
 
0.1%
1612
0.3%
141
 
< 0.1%
134
 
0.1%
1228
0.7%
111
 
< 0.1%
1020
0.5%

bathroom
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.405469366
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-13T13:28:03.608257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.93045624
Coefficient of variation (CV)0.5668693599
Kurtosis17.74517484
Mean3.405469366
Median Absolute Deviation (MAD)1
Skewness3.257083204
Sum12951
Variance3.726661293
MonotonicityNot monotonic
2023-10-13T13:28:03.736385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31112
29.2%
21105
29.1%
4839
22.1%
5299
 
7.9%
1160
 
4.2%
6120
 
3.2%
741
 
1.1%
941
 
1.1%
826
 
0.7%
1222
 
0.6%
Other values (9)38
 
1.0%
ValueCountFrequency (%)
1160
 
4.2%
21105
29.1%
31112
29.2%
4839
22.1%
5299
 
7.9%
6120
 
3.2%
741
 
1.1%
826
 
0.7%
941
 
1.1%
109
 
0.2%
ValueCountFrequency (%)
211
 
< 0.1%
203
 
0.1%
184
 
0.1%
173
 
0.1%
168
 
0.2%
142
 
0.1%
134
 
0.1%
1222
0.6%
114
 
0.1%
109
0.2%

balcony
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size216.7 KiB
3+
1202 
3
1110 
2
925 
1
376 
0
190 

Length

Max length2
Median length1
Mean length1.316066263
Min length1

Characters and Unicode

Total characters5005
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row3+

Common Values

ValueCountFrequency (%)
3+1202
31.6%
31110
29.2%
2925
24.3%
1376
 
9.9%
0190
 
5.0%

Length

2023-10-13T13:28:03.901880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-10-13T13:28:04.053202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
32312
60.8%
2925
24.3%
1376
 
9.9%
0190
 
5.0%

Most occurring characters

ValueCountFrequency (%)
32312
46.2%
+1202
24.0%
2925
18.5%
1376
 
7.5%
0190
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3803
76.0%
Math Symbol1202
 
24.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
32312
60.8%
2925
24.3%
1376
 
9.9%
0190
 
5.0%
Math Symbol
ValueCountFrequency (%)
+1202
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5005
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
32312
46.2%
+1202
24.0%
2925
18.5%
1376
 
7.5%
0190
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32312
46.2%
+1202
24.0%
2925
18.5%
1376
 
7.5%
0190
 
3.8%

floorNum
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct43
Distinct (%)1.1%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.8102537
Minimum0
Maximum51
Zeros134
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-13T13:28:04.209891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.027555066
Coefficient of variation (CV)0.8850705616
Kurtosis4.549322941
Mean6.8102537
Median Absolute Deviation (MAD)3
Skewness1.698733301
Sum25770
Variance36.33142008
MonotonicityNot monotonic
2023-10-13T13:28:04.371966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3513
13.5%
2506
13.3%
1365
 
9.6%
4328
 
8.6%
8197
 
5.2%
6187
 
4.9%
10186
 
4.9%
7183
 
4.8%
5177
 
4.7%
9170
 
4.5%
Other values (33)972
25.6%
ValueCountFrequency (%)
0134
 
3.5%
1365
9.6%
2506
13.3%
3513
13.5%
4328
8.6%
5177
 
4.7%
6187
 
4.9%
7183
 
4.8%
8197
 
5.2%
9170
 
4.5%
ValueCountFrequency (%)
511
 
< 0.1%
451
 
< 0.1%
441
 
< 0.1%
432
0.1%
402
0.1%
392
0.1%
381
 
< 0.1%
352
0.1%
342
0.1%
334
0.1%

facing
Categorical

MISSING

Distinct8
Distinct (%)0.3%
Missing1105
Missing (%)29.1%
Memory size202.8 KiB
East
642 
North-East
639 
North
398 
West
255 
South
233 
Other values (3)
531 

Length

Max length10
Median length5
Mean length6.835804299
Min length4

Characters and Unicode

Total characters18443
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth-West
2nd rowNorth-East
3rd rowNorth-East
4th rowEast
5th rowNorth-East

Common Values

ValueCountFrequency (%)
East642
16.9%
North-East639
16.8%
North398
 
10.5%
West255
 
6.7%
South233
 
6.1%
North-West200
 
5.3%
South-East174
 
4.6%
South-West157
 
4.1%
(Missing)1105
29.1%

Length

2023-10-13T13:28:04.549361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-10-13T13:28:04.723719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
east642
23.8%
north-east639
23.7%
north398
14.8%
west255
 
9.5%
south233
 
8.6%
north-west200
 
7.4%
south-east174
 
6.4%
south-west157
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t3868
21.0%
s2067
11.2%
o1801
9.8%
h1801
9.8%
E1455
 
7.9%
a1455
 
7.9%
N1237
 
6.7%
r1237
 
6.7%
-1170
 
6.3%
W612
 
3.3%
Other values (3)1740
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13405
72.7%
Uppercase Letter3868
 
21.0%
Dash Punctuation1170
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t3868
28.9%
s2067
15.4%
o1801
13.4%
h1801
13.4%
a1455
 
10.9%
r1237
 
9.2%
e612
 
4.6%
u564
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
E1455
37.6%
N1237
32.0%
W612
15.8%
S564
 
14.6%
Dash Punctuation
ValueCountFrequency (%)
-1170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17273
93.7%
Common1170
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t3868
22.4%
s2067
12.0%
o1801
10.4%
h1801
10.4%
E1455
 
8.4%
a1455
 
8.4%
N1237
 
7.2%
r1237
 
7.2%
W612
 
3.5%
e612
 
3.5%
Other values (2)1128
 
6.5%
Common
ValueCountFrequency (%)
-1170
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t3868
21.0%
s2067
11.2%
o1801
9.8%
h1801
9.8%
E1455
 
7.9%
a1455
 
7.9%
N1237
 
6.7%
r1237
 
6.7%
-1170
 
6.3%
W612
 
3.3%
Other values (3)1740
9.4%

agePossession
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size261.5 KiB
Relatively New
1676 
New Property
626 
Moderately Old
575 
Undefined
333 
Old Property
310 

Length

Max length18
Median length14
Mean length13.36760452
Min length9

Characters and Unicode

Total characters50837
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew Property
2nd rowNew Property
3rd rowUnder Construction
4th rowNew Property
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New1676
44.1%
New Property626
 
16.5%
Moderately Old575
 
15.1%
Undefined333
 
8.8%
Old Property310
 
8.2%
Under Construction283
 
7.4%

Length

2023-10-13T13:28:04.899692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-10-13T13:28:05.090429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
new2302
31.7%
relatively1676
23.0%
property936
12.9%
old885
 
12.2%
moderately575
 
7.9%
undefined333
 
4.6%
under283
 
3.9%
construction283
 
3.9%

Most occurring characters

ValueCountFrequency (%)
e8689
17.1%
l4812
 
9.5%
t3753
 
7.4%
3470
 
6.8%
y3187
 
6.3%
r3013
 
5.9%
d2409
 
4.7%
N2302
 
4.5%
w2302
 
4.5%
i2292
 
4.5%
Other values (15)14608
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter40094
78.9%
Uppercase Letter7273
 
14.3%
Space Separator3470
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8689
21.7%
l4812
12.0%
t3753
9.4%
y3187
 
7.9%
r3013
 
7.5%
d2409
 
6.0%
w2302
 
5.7%
i2292
 
5.7%
a2251
 
5.6%
o2077
 
5.2%
Other values (7)5309
13.2%
Uppercase Letter
ValueCountFrequency (%)
N2302
31.7%
R1676
23.0%
P936
12.9%
O885
 
12.2%
U616
 
8.5%
M575
 
7.9%
C283
 
3.9%
Space Separator
ValueCountFrequency (%)
3470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin47367
93.2%
Common3470
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8689
18.3%
l4812
 
10.2%
t3753
 
7.9%
y3187
 
6.7%
r3013
 
6.4%
d2409
 
5.1%
N2302
 
4.9%
w2302
 
4.9%
i2292
 
4.8%
a2251
 
4.8%
Other values (14)12357
26.1%
Common
ValueCountFrequency (%)
3470
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII50837
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e8689
17.1%
l4812
 
9.5%
t3753
 
7.4%
3470
 
6.8%
y3187
 
6.3%
r3013
 
5.9%
d2409
 
4.7%
N2302
 
4.5%
w2302
 
4.5%
i2292
 
4.5%
Other values (15)14608
28.7%

super_built_up_area
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct593
Distinct (%)31.0%
Missing1888
Missing (%)49.6%
Infinite0
Infinite (%)0.0%
Mean1921.658251
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-13T13:28:05.296100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile761.9
Q11457
median1828
Q32215
95-th percentile3187.1
Maximum10000
Range9911
Interquartile range (IQR)758

Descriptive statistics

Standard deviation767.1601693
Coefficient of variation (CV)0.3992177948
Kurtosis10.0830661
Mean1921.658251
Median Absolute Deviation (MAD)372
Skewness1.823228498
Sum3679975.55
Variance588534.7253
MonotonicityNot monotonic
2023-10-13T13:28:05.480720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165038
 
1.0%
195038
 
1.0%
200026
 
0.7%
157825
 
0.7%
215023
 
0.6%
164022
 
0.6%
240820
 
0.5%
190019
 
0.5%
135019
 
0.5%
193018
 
0.5%
Other values (583)1667
43.8%
(Missing)1888
49.6%
ValueCountFrequency (%)
891
< 0.1%
1451
< 0.1%
1611
< 0.1%
2151
< 0.1%
2161
< 0.1%
3251
< 0.1%
3401
< 0.1%
3521
< 0.1%
3801
< 0.1%
4061
< 0.1%
ValueCountFrequency (%)
100001
< 0.1%
69261
< 0.1%
60001
< 0.1%
58002
0.1%
55141
< 0.1%
53502
0.1%
52002
0.1%
48901
< 0.1%
48572
0.1%
48482
0.1%

built_up_area
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct644
Distinct (%)37.2%
Missing2070
Missing (%)54.4%
Infinite0
Infinite (%)0.0%
Mean2360.241413
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-13T13:28:05.674238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile246.4
Q11100
median1650
Q32399
95-th percentile4662
Maximum737147
Range737145
Interquartile range (IQR)1299

Descriptive statistics

Standard deviation17719.60338
Coefficient of variation (CV)7.507538542
Kurtosis1710.107719
Mean2360.241413
Median Absolute Deviation (MAD)642
Skewness41.21758008
Sum4090298.369
Variance313984343.9
MonotonicityNot monotonic
2023-10-13T13:28:05.852881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180041
 
1.1%
324037
 
1.0%
190034
 
0.9%
135034
 
0.9%
270033
 
0.9%
90028
 
0.7%
160026
 
0.7%
200025
 
0.7%
130025
 
0.7%
170023
 
0.6%
Other values (634)1427
37.5%
(Missing)2070
54.4%
ValueCountFrequency (%)
21
 
< 0.1%
141
 
< 0.1%
301
 
< 0.1%
331
 
< 0.1%
503
0.1%
531
 
< 0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
605
0.1%
ValueCountFrequency (%)
7371471
 
< 0.1%
135001
 
< 0.1%
112861
 
< 0.1%
95001
 
< 0.1%
90007
0.2%
87751
 
< 0.1%
82861
 
< 0.1%
8067.81
 
< 0.1%
80001
 
< 0.1%
75002
 
0.1%

carpet_area
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct733
Distinct (%)37.7%
Missing1859
Missing (%)48.9%
Infinite0
Infinite (%)0.0%
Mean2483.466943
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-13T13:28:06.052324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile348.3
Q1824
median1294
Q31786.25
95-th percentile2945.8
Maximum607936
Range607921
Interquartile range (IQR)962.25

Descriptive statistics

Standard deviation22375.23929
Coefficient of variation (CV)9.009678729
Kurtosis627.8393573
Mean2483.466943
Median Absolute Deviation (MAD)472
Skewness24.7960836
Sum4827859.738
Variance500651333.4
MonotonicityNot monotonic
2023-10-13T13:28:06.223426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140042
 
1.1%
180036
 
0.9%
160036
 
0.9%
120032
 
0.8%
150030
 
0.8%
135028
 
0.7%
165028
 
0.7%
145023
 
0.6%
130023
 
0.6%
100022
 
0.6%
Other values (723)1644
43.2%
(Missing)1859
48.9%
ValueCountFrequency (%)
151
 
< 0.1%
331
 
< 0.1%
481
 
< 0.1%
501
 
< 0.1%
591
 
< 0.1%
601
 
< 0.1%
661
 
< 0.1%
721
 
< 0.1%
76.443
0.1%
77.312
0.1%
ValueCountFrequency (%)
6079361
< 0.1%
5692431
< 0.1%
5143961
< 0.1%
645291
< 0.1%
644121
< 0.1%
581411
< 0.1%
549171
< 0.1%
488111
< 0.1%
459661
< 0.1%
344011
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size215.5 KiB
0
3082 
1
721 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Length

2023-10-13T13:28:06.385898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-10-13T13:28:06.925456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Most occurring characters

ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common3803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

servant room
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size215.5 KiB
0
2446 
1
1357 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Length

2023-10-13T13:28:07.040999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-10-13T13:28:07.174946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Most occurring characters

ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Most occurring scripts

ValueCountFrequency (%)
Common3803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02446
64.3%
11357
35.7%

store room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size215.5 KiB
0
3459 
1
 
344

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Length

2023-10-13T13:28:07.289072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-10-13T13:28:07.427045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Most occurring characters

ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common3803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size215.5 KiB
0
3140 
1
663 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Length

2023-10-13T13:28:07.537070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-10-13T13:28:07.680206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Most occurring characters

ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common3803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size215.5 KiB
0
3382 
1
421 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Length

2023-10-13T13:28:07.808943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-10-13T13:28:07.947748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Most occurring characters

ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common3803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size215.5 KiB
0
2509 
1
1078 
2
 
216

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
02509
66.0%
11078
28.3%
2216
 
5.7%

Length

2023-10-13T13:28:08.062273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-10-13T13:28:08.217639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
02509
66.0%
11078
28.3%
2216
 
5.7%

Most occurring characters

ValueCountFrequency (%)
02509
66.0%
11078
28.3%
2216
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02509
66.0%
11078
28.3%
2216
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common3803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02509
66.0%
11078
28.3%
2216
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02509
66.0%
11078
28.3%
2216
 
5.7%

luxury_score
Real number (ℝ≥0)

ZEROS

Distinct161
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.94793584
Minimum0
Maximum174
Zeros486
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-13T13:28:08.363800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median58
Q3109
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)78

Descriptive statistics

Standard deviation52.82178929
Coefficient of variation (CV)0.7445148144
Kurtosis-0.8553365481
Mean70.94793584
Median Absolute Deviation (MAD)37
Skewness0.4702883943
Sum269815
Variance2790.141423
MonotonicityNot monotonic
2023-10-13T13:28:08.548142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0486
 
12.8%
49353
 
9.3%
174196
 
5.2%
4462
 
1.6%
3858
 
1.5%
7256
 
1.5%
16555
 
1.4%
6050
 
1.3%
3749
 
1.3%
4246
 
1.2%
Other values (151)2392
62.9%
ValueCountFrequency (%)
0486
12.8%
56
 
0.2%
66
 
0.2%
743
 
1.1%
830
 
0.8%
99
 
0.2%
127
 
0.2%
1310
 
0.3%
1412
 
0.3%
1543
 
1.1%
ValueCountFrequency (%)
174196
5.2%
1691
 
< 0.1%
1689
 
0.2%
16721
 
0.6%
16611
 
0.3%
16555
 
1.4%
1613
 
0.1%
16028
 
0.7%
15923
 
0.6%
15834
 
0.9%

Interactions

2023-10-13T13:27:58.087488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:42.130295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:43.828247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:45.964789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:47.554215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:49.371560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:51.126355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:52.734983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:54.414356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:56.450316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:58.230190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:42.296342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:44.017056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:46.157949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:47.729948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:49.539499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:51.277157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:52.889428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:54.574744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:56.608966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:58.382615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:42.450015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:44.187718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:46.307519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:47.901067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:49.735477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:51.442997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:53.065598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:54.733985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:56.791497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:58.529998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:42.610123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:44.342018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:46.449992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:48.046369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:49.904037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:51.580383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:53.242196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:54.888073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:56.968925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:58.695446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:42.768170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:44.529864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:46.609983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:48.295760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:50.098168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:51.749379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:53.417144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:55.062853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:57.156199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:58.875070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:42.940326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:44.722351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:46.759046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:48.499121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:50.275776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:51.909357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:53.594655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:55.255865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:57.315854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:59.029230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:43.095049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:45.155038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:46.890019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:48.659846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:50.437820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:52.074971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:53.752928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:55.405860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:57.473032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:59.199905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:43.241159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:45.359847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:47.055733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:48.828231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:50.605199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:52.246158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:53.933532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:55.905969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:57.635914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:59.357805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:43.426446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:45.558151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:47.230865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:49.013184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:50.785813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:52.407797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:54.088065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:56.078265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:57.775696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:59.520432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:43.623119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:45.736402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:47.397095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:49.203143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:50.953872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:52.569420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:54.264141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:56.237490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-13T13:27:57.926020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-10-13T13:28:08.713014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-10-13T13:28:08.999087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-10-13T13:28:09.285230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-10-13T13:28:09.587688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-10-13T13:28:09.815970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-10-13T13:27:59.793160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-13T13:28:00.454842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-13T13:28:00.785579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-10-13T13:28:01.028810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatsignature global park 4sector 360.827585.01081.0Super Built up area 1081(100.43 sq.m.)Carpet area: 650 sq.ft. (60.39 sq.m.)3222.0NaNNew Property1081.0NaN650.00000008
1flatsmart world gemssector 890.958600.01105.0Carpet area: 1103 (102.47 sq.m.)2224.0NaNNew PropertyNaNNaN1103.011000038
2flatpyramid elitesector 860.4679.058228.0Carpet area: 58141 (5401.48 sq.m.)2210.0NaNUnder ConstructionNaNNaN58141.000000015
3flatbreez global hill viewsohna road0.325470.0585.0Built Up area: 1000 (92.9 sq.m.)Carpet area: 585 sq.ft. (54.35 sq.m.)22117.0NaNNew PropertyNaN1000.00585.000000049
4flatbestech park view sanskrutisector 921.608020.01995.0Super Built up area 1995(185.34 sq.m.)Built Up area: 1615 sq.ft. (150.04 sq.m.)Carpet area: 1476 sq.ft. (137.12 sq.m.)343+10.0North-WestRelatively New1995.01615.001476.0010011174
5flatsuncity avenuesector 1020.489022.0532.0Super Built up area 632(58.71 sq.m.)Carpet area: 532 sq.ft. (49.42 sq.m.)2215.0North-EastRelatively New632.0NaN532.0001000159
6flatparas quartiergwal pahari7.5014018.05350.0Super Built up area 5350(497.03 sq.m.)443+20.0North-EastNew Property5350.0NaNNaN01011149
7flatexperion the heartsongsector 1082.008554.02338.0Super Built up area 2338(217.21 sq.m.)333+14.0EastRelatively New2338.0NaNNaN01000095
8flatadani m2k oyster grandesector 1021.909105.02087.0Super Built up area 1889(175.49 sq.m.)3438.0North-EastRelatively New1889.0NaNNaN010000165
9houseindependentsector 1051.2010122.01186.0Plot area 1185.51(110.14 sq.m.)6212.0North-WestOld PropertyNaN1185.51NaN0000009

Last rows

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3793flatgls arawali homessohna road0.274687.0576.0Carpet area: 576 (53.51 sq.m.)2221.0EastNew PropertyNaNNaN576.000000035
3794houseindependentsector 278.0026298.03042.0Plot area 338(282.61 sq.m.)9934.0North-EastRelatively NewNaN3042.0NaN111102110
3795flateldeco accoladesohna road0.875965.01459.0Super Built up area 1457(135.36 sq.m.)Carpet area: 849 sq.ft. (78.87 sq.m.)223+10.0NaNRelatively New1457.0NaN849.010000072
3796flatparas dewssector 1060.926642.01385.0Super Built up area 1385(128.67 sq.m.)Built Up area: 940 sq.ft. (87.33 sq.m.)Carpet area: 845 sq.ft. (78.5 sq.m.)223+2.0EastRelatively New1385.0940.0845.0000000174
3797housesurendra homes dayaindependentd colonysector 60.7515625.0480.0Built Up area: 480 (44.59 sq.m.)4421.0NaNUndefinedNaN480.0NaN0000000
3798flatpivotal devaansector 840.376346.0583.0Super Built up area 583(54.16 sq.m.)Carpet area: 483 sq.ft. (44.87 sq.m.)2215.0North-WestRelatively New583.0NaN483.000000073
3799houseinternational city by sobha phase 1sector 1096.009634.06228.0Plot area 692(578.6 sq.m.)553+2.0South-WestRelatively NewNaN6228.0NaN111100160
3800flatansal api celebrity suitessector 20.608163.0735.0Super Built up area 735(68.28 sq.m.)1115.0North-EastModerately Old735.0NaNNaN00000167
3801houseindependentsector 4315.5028233.05490.0Plot area 610(510.04 sq.m.)5633.0EastModerately OldNaN5490.0NaN11110076
3802flatm3m ikonicsector 681.789128.01950.0Super Built up area 1950(181.16 sq.m.)Built Up area: 1845 sq.ft. (171.41 sq.m.)Carpet area: 1530 sq.ft. (142.14 sq.m.)333+27.0SouthRelatively New1950.01845.01530.0000001126

Duplicate rows

Most frequently occurring

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score# duplicates
0flatambience caitrionasector 2414.00200000.0700.0Built Up area: 700 (65.03 sq.m.)4533.0EastUndefinedNaN700.0NaN00000002
1flatansal heights 86sector 860.905325.01690.0Built Up area: 1690 (157.01 sq.m.)33210.0NaNNew PropertyNaN1690.0NaN000000292
2flatansal heights 86sector 861.304666.02786.0Super Built up area 2786(258.83 sq.m.)46211.0EastNew Property2786.0NaNNaN010010862
3flatansal housing highland parksector 1030.886429.01369.0Super Built up area 1361(126.44 sq.m.)2233.0NaNNew Property1361.0NaNNaN000000522
4flatantriksh heightssector 840.855556.01530.0Super Built up area 1350(125.42 sq.m.)22310.0North-WestNew Property1350.0NaNNaN100010242
5flatapartmentsector 920.754687.01600.0Carpet area: 1600 (148.64 sq.m.)3432.0EastModerately OldNaNNaN1600.01000001132
6flatashiana anmolsohna road0.8811125.0791.0Super Built up area 1275(118.45 sq.m.)Carpet area: 791 sq.ft. (73.49 sq.m.)22213.0EastRelatively New1275.0NaN791.00000021272
7flatassotech blithsector 990.926739.01365.0Super Built up area 1365(126.81 sq.m.)223+22.0NaNUnder Construction1365.0NaNNaN000000562
8flatassotech blithsector 991.906702.02835.0Built Up area: 2835 (263.38 sq.m.)443+2.0North-EastUndefinedNaN2835.0NaN000000512
9flatats tourmalinesector 1092.308897.02585.0Super Built up area 2585(240.15 sq.m.)343+10.0EastNew Property2585.0NaNNaN010010742